Epic signal! Predicted market trading volume surpasses $279 billion. The flow of this "smart money" will completely overturn your perception of $BTC and $ETH!

Imagine this scene: hundreds of millions of people watching the Super Bowl broadcast on TV, while on another screen, trades are wildly fluctuating. Bets range from who will be the champion to the exact passing yardage of a quarterback. Last year, the total trading volume of such prediction markets reached $279 billion.

Is this just sports? No. Economic policies, product launches, and even solving mathematical puzzles have become trading targets. Market observers have long debated its nature: is it gambling, trading, or some higher-level information tool? As a veteran who has studied markets for twenty years, my view is simple: prediction markets are the market itself. And the market is the ultimate machine for allocating resources and integrating information.

Its operating logic is clear: issue assets linked to specific events, and pay out when the event occurs. People bet real money on their judgments. From a market design perspective, this is closer to the truth than trusting any single commentator or even looking at Las Vegas betting odds.

The core of traditional betting is balancing stakes to attract bets on underdogs. Prediction markets, however, incentivize trading based on real information. Want to know the probability of a new tariff being implemented? Derive it from soybean futures prices—an intricate and noisy process. But if you ask directly in a prediction market, the answer is much more intuitive.

This model’s prototype appeared in 16th-century Europe, where people even bet on the next Pope. The modern version is rooted in economics, statistics, and computer science. In the 1980s, Charles Plott and Shyam Sunder laid the academic foundation, followed by the emergence of Iowa Electronic Markets.

The mechanism is actually quite simple. For example, a market might issue a contract on “Will Seahawks quarterback Sam Darnold throw a pass within the opponent’s 1-yard line?” Each contract pays $1 if the event occurs. The market price of the contract directly reflects the probability of the event. A price of $0.5 indicates a 50% chance.

If you estimate the probability at 67%, you buy. Your purchase pushes the price up, signaling to the market that the “likelihood is underestimated.” Conversely, selling pushes the price down. When the market functions well, its advantage is overwhelming.

Public opinion polls are just static views of proportions; converting them into probabilities requires complex statistical inference. Prediction markets update dynamically, continuously evolving with new information and participants. Most importantly, they have clear incentives. Traders “get involved,” so they must carefully handle information and only take risks in areas they understand.

Your information and expertise can be directly monetized. This encourages deeper exploration. Prediction markets also cover far more than traditional tools. Someone with information influencing oil demand can profit through crude oil futures. But many outcomes we want to predict are hard to approach in stock or commodity markets.

Today, there are markets dedicated to aggregating judgments to predict the timing of solving specific mathematical problems—crucial for measuring AI development levels.

Of course, there are significant issues as well. Infrastructure-wise: how to verify events and reach market consensus? How to ensure transparency and auditability? From a market design perspective: participation must be limited to those with relevant information; otherwise, prices lack signals. The market before the Brexit referendum is a counterexample—insiders did not participate sufficiently.

More challenging is insider information. If Seahawks’ offensive coordinator knows and can influence the passing decision, his involvement would undermine fairness. Potential participants who believe there’s inside info might rationally stay out, causing the market to collapse.

Manipulation risks also exist. Someone could turn a tool that aggregates public wisdom into a weapon for manipulating opinion—for example, using campaign funds to inflate the probability of their candidate winning. Fortunately, prediction markets have some self-correcting ability; valuations that deviate from rationality tend to attract counter-manipulation.

Therefore, prediction market platforms must enhance transparency and clearly disclose rules regarding participant management and contract design. If these issues are addressed, the role of prediction markets in future information integration will be limitless. They offer a way to efficiently price and transform dispersed, even specialized, knowledge into public signals.

This could be a paradigm-level tool for understanding the complex world—whether in sports, politics, technology, or the market sentiment and fundamentals behind $BTC and $ETH.

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